Profiling_Report

Dataset statistics

Number of variables	10
Number of observations	499
Missing cells	0
Missing cells (%)	0.0%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	29.5 KiB
Average record size in memory	60.5 B

Variable types

Numeric	7
Categorical	3

Alerts

`Ordenes_Correctivo` is highly overall correlated with `Total_Ordenes`	High correlation
`Ordenes_Preventivo` is highly overall correlated with `Total_Ordenes`	High correlation
`Total_Ordenes` is highly overall correlated with `Ordenes_Correctivo` and 1 other fields	High correlation
`ID_Equipo` is uniformly distributed	Uniform
`ID_Equipo` has unique values	Unique

Reproduction

Analysis started	2025-02-21 09:23:05.532989
Analysis finished	2025-02-21 09:23:17.963453
Duration	12.43 seconds
Software version	ydata-profiling vv4.12.2
Download configuration	config.json

ID_Equipo
Real number (ℝ)

Uniform Unique

Distinct	499
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	250

Minimum	1
Maximum	499
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	4.0 KiB

Quantile statistics

Minimum	1
5-th percentile	25.9
Q1	125.5
median	250
Q3	374.5
95-th percentile	474.1
Maximum	499
Range	498
Interquartile range (IQR)	249

Descriptive statistics

Standard deviation	144.19316
Coefficient of variation (CV)	0.57677263
Kurtosis	-1.2
Mean	250
Median Absolute Deviation (MAD)	125
Skewness	0
Sum	124750
Variance	20791.667
Monotonicity	Strictly increasing

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
1	1	0.2%
329	1	0.2%
342	1	0.2%
341	1	0.2%
340	1	0.2%
339	1	0.2%
338	1	0.2%
337	1	0.2%
336	1	0.2%
335	1	0.2%
Other values (489)	489	98.0%

Value	Count	Frequency (%)
1	1	0.2%
2	1	0.2%
3	1	0.2%
4	1	0.2%
5	1	0.2%
6	1	0.2%
7	1	0.2%
8	1	0.2%
9	1	0.2%
10	1	0.2%

Value	Count	Frequency (%)
499	1	0.2%
498	1	0.2%
497	1	0.2%
496	1	0.2%
495	1	0.2%
494	1	0.2%
493	1	0.2%
492	1	0.2%
491	1	0.2%
490	1	0.2%

Tipo_Equipo
Categorical

Distinct	4
Distinct (%)	0.8%
Missing	0
Missing (%)	0.0%
Memory size	831.0 B

Generador	135
Transformador	125
Compresor	121
Motor	118

Length

Max length	13
Median length	9
Mean length	9.0561122
Min length	5

Characters and Unicode

Total characters	4519
Distinct characters	15
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Motor
2nd row	Motor
3rd row	Motor
4th row	Transformador
5th row	Compresor

Common Values

Value	Count	Frequency (%)
Generador	135	27.1%
Transformador	125	25.1%
Compresor	121	24.2%
Motor	118	23.6%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
generador	135	27.1%
transformador	125	25.1%
compresor	121	24.2%
motor	118	23.6%

Most occurring characters

Value	Count	Frequency (%)
r	1005	22.2%
o	863	19.1%
e	391	8.7%
a	385	8.5%
n	260	5.8%
d	260	5.8%
s	246	5.4%
m	246	5.4%
G	135	3.0%
T	125	2.8%
Other values (5)	603	13.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	4519	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
r	1005	22.2%
o	863	19.1%
e	391	8.7%
a	385	8.5%
n	260	5.8%
d	260	5.8%
s	246	5.4%
m	246	5.4%
G	135	3.0%
T	125	2.8%
Other values (5)	603	13.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	4519	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
r	1005	22.2%
o	863	19.1%
e	391	8.7%
a	385	8.5%
n	260	5.8%
d	260	5.8%
s	246	5.4%
m	246	5.4%
G	135	3.0%
T	125	2.8%
Other values (5)	603	13.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	4519	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
r	1005	22.2%
o	863	19.1%
e	391	8.7%
a	385	8.5%
n	260	5.8%
d	260	5.8%
s	246	5.4%
m	246	5.4%
G	135	3.0%
T	125	2.8%
Other values (5)	603	13.3%

Fabricante
Categorical

Distinct	4
Distinct (%)	0.8%
Missing	0
Missing (%)	0.0%
Memory size	831.0 B

ABB	131
Siemens	129
GE	123
Schneider	116

Length

Max length	9
Median length	7
Mean length	5.1823647
Min length	2

Characters and Unicode

Total characters	2586
Distinct characters	14
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Schneider
2nd row	ABB
3rd row	GE
4th row	Siemens
5th row	ABB

Common Values

Value	Count	Frequency (%)
ABB	131	26.3%
Siemens	129	25.9%
GE	123	24.6%
Schneider	116	23.2%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
abb	131	26.3%
siemens	129	25.9%
ge	123	24.6%
schneider	116	23.2%

Most occurring characters

Value	Count	Frequency (%)
e	490	18.9%
B	262	10.1%
S	245	9.5%
i	245	9.5%
n	245	9.5%
A	131	5.1%
m	129	5.0%
s	129	5.0%
G	123	4.8%
E	123	4.8%
Other values (4)	464	17.9%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	2586	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
e	490	18.9%
B	262	10.1%
S	245	9.5%
i	245	9.5%
n	245	9.5%
A	131	5.1%
m	129	5.0%
s	129	5.0%
G	123	4.8%
E	123	4.8%
Other values (4)	464	17.9%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	2586	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
e	490	18.9%
B	262	10.1%
S	245	9.5%
i	245	9.5%
n	245	9.5%
A	131	5.1%
m	129	5.0%
s	129	5.0%
G	123	4.8%
E	123	4.8%
Other values (4)	464	17.9%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	2586	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
e	490	18.9%
B	262	10.1%
S	245	9.5%
i	245	9.5%
n	245	9.5%
A	131	5.1%
m	129	5.0%
s	129	5.0%
G	123	4.8%
E	123	4.8%
Other values (4)	464	17.9%

Modelo
Categorical

Distinct	4
Distinct (%)	0.8%
Missing	0
Missing (%)	0.0%
Memory size	831.0 B

Z300	141
Y200	126
X100	121
M400	111

Length

Max length	4
Median length	4
Mean length	4
Min length	4

Characters and Unicode

Total characters	1996
Distinct characters	9
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	X100
2nd row	Z300
3rd row	Y200
4th row	X100
5th row	Y200

Common Values

Value	Count	Frequency (%)
Z300	141	28.3%
Y200	126	25.3%
X100	121	24.2%
M400	111	22.2%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
z300	141	28.3%
y200	126	25.3%
x100	121	24.2%
m400	111	22.2%

Most occurring characters

Value	Count	Frequency (%)
0	998	50.0%
Z	141	7.1%
3	141	7.1%
Y	126	6.3%
2	126	6.3%
X	121	6.1%
1	121	6.1%
M	111	5.6%
4	111	5.6%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	1996	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
0	998	50.0%
Z	141	7.1%
3	141	7.1%
Y	126	6.3%
2	126	6.3%
X	121	6.1%
1	121	6.1%
M	111	5.6%
4	111	5.6%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	1996	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
0	998	50.0%
Z	141	7.1%
3	141	7.1%
Y	126	6.3%
2	126	6.3%
X	121	6.1%
1	121	6.1%
M	111	5.6%
4	111	5.6%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	1996	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
0	998	50.0%
Z	141	7.1%
3	141	7.1%
Y	126	6.3%
2	126	6.3%
X	121	6.1%
1	121	6.1%
M	111	5.6%
4	111	5.6%

Potencia_kW
Real number (ℝ)

Distinct	471
Distinct (%)	94.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	2420.6553

Minimum	-100
Maximum	4997
Zeros	0
Zeros (%)	0.0%
Negative	10
Negative (%)	2.0%
Memory size	4.0 KiB

Quantile statistics

Minimum	-100
5-th percentile	249.8
Q1	1233
median	2406
Q3	3563.5
95-th percentile	4786.1
Maximum	4997
Range	5097
Interquartile range (IQR)	2330.5

Descriptive statistics

Standard deviation	1443.2171
Coefficient of variation (CV)	0.59620927
Kurtosis	-1.1039458
Mean	2420.6553
Median Absolute Deviation (MAD)	1168
Skewness	0.054996689
Sum	1207907
Variance	2082875.7
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
-100	10	2.0%
562	3	0.6%
799	2	0.4%
1536	2	0.4%
670	2	0.4%
4948	2	0.4%
3212	2	0.4%
404	2	0.4%
1514	2	0.4%
4080	2	0.4%
Other values (461)	470	94.2%

Value	Count	Frequency (%)
-100	10	2.0%
53	1	0.2%
79	1	0.2%
137	1	0.2%
146	1	0.2%
153	1	0.2%
163	1	0.2%
173	1	0.2%
187	1	0.2%
197	1	0.2%

Value	Count	Frequency (%)
4997	1	0.2%
4993	1	0.2%
4992	1	0.2%
4974	1	0.2%
4948	2	0.4%
4947	1	0.2%
4945	1	0.2%
4930	1	0.2%
4929	1	0.2%
4914	1	0.2%

Horas_Recomendadas_Revision
Real number (ℝ)

Distinct	489
Distinct (%)	98.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	5225.1603

Minimum	525
Maximum	9993
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	4.0 KiB

Quantile statistics

Minimum	525
5-th percentile	855.5
Q1	2896
median	5258
Q3	7451.5
95-th percentile	9478.4
Maximum	9993
Range	9468
Interquartile range (IQR)	4555.5

Descriptive statistics

Standard deviation	2744.958
Coefficient of variation (CV)	0.5253347
Kurtosis	-1.1560925
Mean	5225.1603
Median Absolute Deviation (MAD)	2259
Skewness	-0.029294541
Sum	2607355
Variance	7534794.5
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
655	2	0.4%
1783	2	0.4%
2270	2	0.4%
8101	2	0.4%
2386	2	0.4%
9026	2	0.4%
1717	2	0.4%
6992	2	0.4%
5938	2	0.4%
1534	2	0.4%
Other values (479)	479	96.0%

Value	Count	Frequency (%)
525	1	0.2%
549	1	0.2%
564	1	0.2%
572	1	0.2%
585	1	0.2%
618	1	0.2%
654	1	0.2%
655	2	0.4%
661	1	0.2%
663	1	0.2%

Value	Count	Frequency (%)
9993	1	0.2%
9933	1	0.2%
9926	1	0.2%
9921	1	0.2%
9916	1	0.2%
9909	1	0.2%
9881	1	0.2%
9851	1	0.2%
9836	1	0.2%
9829	1	0.2%

Total_Ordenes
Real number (ℝ)

High correlation

Distinct	29
Distinct (%)	5.8%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	19.93988

Minimum	7
Maximum	36
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	4.0 KiB

Quantile statistics

Minimum	7
5-th percentile	13
Q1	17
median	20
Q3	23
95-th percentile	28
Maximum	36
Range	29
Interquartile range (IQR)	6

Descriptive statistics

Standard deviation	4.5654975
Coefficient of variation (CV)	0.22896314
Kurtosis	0.068828658
Mean	19.93988
Median Absolute Deviation (MAD)	3
Skewness	0.31398371
Sum	9950
Variance	20.843768
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=29)

Value	Count	Frequency (%)
18	47	9.4%
19	43	8.6%
21	39	7.8%
20	37	7.4%
17	37	7.4%
16	36	7.2%
24	36	7.2%
23	35	7.0%
15	34	6.8%
22	29	5.8%
Other values (19)	126	25.3%

Value	Count	Frequency (%)
7	1	0.2%
8	1	0.2%
9	1	0.2%
10	1	0.2%
11	5	1.0%
12	10	2.0%
13	12	2.4%
14	19	3.8%
15	34	6.8%
16	36	7.2%

Value	Count	Frequency (%)
36	1	0.2%
34	1	0.2%
33	1	0.2%
32	2	0.4%
31	3	0.6%
30	6	1.2%
29	6	1.2%
28	7	1.4%
27	12	2.4%
26	16	3.2%

Ordenes_Correctivo
Real number (ℝ)

High correlation

Distinct	20
Distinct (%)	4.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	10.138277

Minimum	1
Maximum	21
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	4.0 KiB

Quantile statistics

Minimum	1
5-th percentile	5
Q1	8
median	10
Q3	12
95-th percentile	15.1
Maximum	21
Range	20
Interquartile range (IQR)	4

Descriptive statistics

Standard deviation	3.2608974
Coefficient of variation (CV)	0.32164218
Kurtosis	-0.0018880075
Mean	10.138277
Median Absolute Deviation (MAD)	2
Skewness	0.27670445
Sum	5059
Variance	10.633452
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=20)

Value	Count	Frequency (%)
10	59	11.8%
9	56	11.2%
11	56	11.2%
8	52	10.4%
12	49	9.8%
7	44	8.8%
13	41	8.2%
14	34	6.8%
6	28	5.6%
5	22	4.4%
Other values (10)	58	11.6%

Value	Count	Frequency (%)
1	1	0.2%
3	1	0.2%
4	14	2.8%
5	22	4.4%
6	28	5.6%
7	44	8.8%
8	52	10.4%
9	56	11.2%
10	59	11.8%
11	56	11.2%

Value	Count	Frequency (%)
21	1	0.2%
20	2	0.4%
19	3	0.6%
18	3	0.6%
17	7	1.4%
16	9	1.8%
15	17	3.4%
14	34	6.8%
13	41	8.2%
12	49	9.8%

Ordenes_Preventivo
Real number (ℝ)

High correlation

Distinct	19
Distinct (%)	3.8%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	9.8016032

Minimum	2
Maximum	23
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	4.0 KiB

Quantile statistics

Minimum	2
5-th percentile	5
Q1	8
median	10
Q3	12
95-th percentile	15
Maximum	23
Range	21
Interquartile range (IQR)	4

Descriptive statistics

Standard deviation	3.1949253
Coefficient of variation (CV)	0.32595946
Kurtosis	0.3073908
Mean	9.8016032
Median Absolute Deviation (MAD)	2
Skewness	0.26313678
Sum	4891
Variance	10.207548
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=19)

Value	Count	Frequency (%)
10	72	14.4%
11	66	13.2%
9	54	10.8%
12	51	10.2%
8	50	10.0%
6	37	7.4%
7	36	7.2%
13	30	6.0%
5	26	5.2%
14	22	4.4%
Other values (9)	55	11.0%

Value	Count	Frequency (%)
2	3	0.6%
3	4	0.8%
4	13	2.6%
5	26	5.2%
6	37	7.4%
7	36	7.2%
8	50	10.0%
9	54	10.8%
10	72	14.4%
11	66	13.2%

Value	Count	Frequency (%)
23	1	0.2%
19	1	0.2%
18	5	1.0%
17	6	1.2%
16	11	2.2%
15	11	2.2%
14	22	4.4%
13	30	6.0%
12	51	10.2%
11	66	13.2%

Vida_util_estimada
Real number (ℝ)

Distinct	487
Distinct (%)	97.6%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	94637.06

Minimum	70524
Maximum	99982
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	4.0 KiB

Quantile statistics

Minimum	70524
5-th percentile	85025.1
Q1	92276
median	96218
Q3	98421
95-th percentile	99751.9
Maximum	99982
Range	29458
Interquartile range (IQR)	6145

Descriptive statistics

Standard deviation	5040.6204
Coefficient of variation (CV)	0.053262648
Kurtosis	3.5753992
Mean	94637.06
Median Absolute Deviation (MAD)	2777
Skewness	-1.6436326
Sum	47223893
Variance	25407854
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
99333	2	0.4%
94216	2	0.4%
89711	2	0.4%
99935	2	0.4%
86426	2	0.4%
93986	2	0.4%
99079	2	0.4%
98035	2	0.4%
99861	2	0.4%
94122	2	0.4%
Other values (477)	479	96.0%

Value	Count	Frequency (%)
70524	1	0.2%
70889	1	0.2%
71579	1	0.2%
72510	1	0.2%
75876	1	0.2%
77730	1	0.2%
78763	1	0.2%
79409	1	0.2%
79443	1	0.2%
79794	1	0.2%

Value	Count	Frequency (%)
99982	1	0.2%
99939	1	0.2%
99935	2	0.4%
99933	1	0.2%
99905	1	0.2%
99898	1	0.2%
99890	1	0.2%
99881	1	0.2%
99874	1	0.2%
99872	1	0.2%

	Fabricante	Horas_Recomendadas_Revision	ID_Equipo	Modelo	Ordenes_Correctivo	Ordenes_Preventivo	Potencia_kW	Tipo_Equipo	Total_Ordenes	Vida_util_estimada
Fabricante	1.000	0.060	0.087	0.032	0.000	0.080	0.035	0.076	0.000	0.065
Horas_Recomendadas_Revision	0.060	1.000	0.016	0.000	0.006	0.005	0.083	0.000	0.013	-0.040
ID_Equipo	0.087	0.016	1.000	0.051	-0.012	0.013	0.004	0.000	0.009	-0.065
Modelo	0.032	0.000	0.051	1.000	0.000	0.000	0.000	0.000	0.018	0.096
Ordenes_Correctivo	0.000	0.006	-0.012	0.000	1.000	0.001	-0.052	0.000	0.698	0.003
Ordenes_Preventivo	0.080	0.005	0.013	0.000	0.001	1.000	0.004	0.070	0.684	0.046
Potencia_kW	0.035	0.083	0.004	0.000	-0.052	0.004	1.000	0.000	-0.041	-0.056
Tipo_Equipo	0.076	0.000	0.000	0.000	0.000	0.070	0.000	1.000	0.000	0.000
Total_Ordenes	0.000	0.013	0.009	0.018	0.698	0.684	-0.041	0.000	1.000	0.020
Vida_util_estimada	0.065	-0.040	-0.065	0.096	0.003	0.046	-0.056	0.000	0.020	1.000

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

	ID_Equipo	Tipo_Equipo	Fabricante	Modelo	Potencia_kW	Horas_Recomendadas_Revision	Total_Ordenes	Ordenes_Correctivo	Ordenes_Preventivo	Vida_util_estimada
0	1	Motor	Schneider	X100	1009	9656	20	10	10	92645.0
1	2	Motor	ABB	Z300	1220	2165	13	7	6	91899.0
2	3	Motor	GE	Y200	3733	6674	16	10	6	97734.0
3	4	Transformador	Siemens	X100	662	1480	20	12	8	93640.0
4	5	Compresor	ABB	Y200	982	4282	17	10	7	99898.0
5	6	Compresor	GE	Z300	4426	8747	24	13	11	84467.0
6	7	Transformador	ABB	Y200	1830	1238	11	5	6	93947.0
7	8	Transformador	GE	Y200	532	3846	27	16	11	93045.0
8	9	Compresor	ABB	X100	3484	9688	16	12	4	79443.0
9	10	Compresor	Schneider	M400	3227	5305	18	10	8	98758.0

	ID_Equipo	Tipo_Equipo	Fabricante	Modelo	Potencia_kW	Horas_Recomendadas_Revision	Total_Ordenes	Ordenes_Correctivo	Ordenes_Preventivo	Vida_util_estimada
489	490	Transformador	Siemens	X100	-100	5118	14	4	10	92265.0
490	491	Transformador	Siemens	Z300	3507	5258	19	13	6	95653.0
491	492	Generador	Schneider	M400	250	3083	22	14	8	94216.0
492	493	Transformador	Siemens	Z300	2889	7172	18	9	9	99027.0
493	494	Transformador	GE	Z300	187	5331	15	8	7	94081.0
494	495	Compresor	Schneider	Y200	4383	6188	23	12	11	98566.0
495	496	Motor	Siemens	X100	3617	5594	21	10	11	90614.0
496	497	Generador	Schneider	Y200	548	8614	22	11	11	96443.0
497	498	Motor	Schneider	M400	2602	5874	21	10	11	91463.0
498	499	Motor	ABB	M400	877	6907	15	4	11	98945.0

Overview

Variables

Common Values

Length

Common Values (Plot)

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Common Values

Length

Common Values (Plot)

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Common Values

Length

Common Values (Plot)

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Interactions

Correlations

Missing values

Sample